# -*- coding: utf-8 -*-
from datetime import timedelta

from jms.dwd.dwd_s01_whole_operations_trace_dt import jms_dwd__dwd_s01_whole_operations_trace_dt
from utils.operators.spark_sql_operator import SparkSqlOperator

jms_dwd__dwd_s02_whole_operations_trace_step_dt = SparkSqlOperator(
    task_id='jms_dwd__dwd_s02_whole_operations_trace_step_dt',
    task_concurrency=1,
    pool_slots=9,
    master='yarn',
    name='jms_dwd__dwd_s02_whole_operations_trace_step_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dwd/dwd_s02_whole_operations_trace_step_dt/execute.hql',
    driver_memory='30G',
    driver_cores=10,
    email=['rongguangfan@jtexpress.com','yl_bigdata@yl-scm.com'],
    executor_cores=8,
    executor_memory='24G',
    num_executors=20,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 120,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 180,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '3G',  # 堆外内存
          'spark.sql.shuffle.partitions': 3600,
          'spark.default.paralleism': 3600,
          'spark.hadoop.hive.exec.dynamic.partition.mode': 'true',
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions': 60,  # 每天生成 60 个分区
              'hive.exec.max.dynamic.partitions.pernode': 60,  # 每天生成 60 个分区
              },
    yarn_queue='pro',
    execution_timeout=timedelta(hours=3),
)

jms_dwd__dwd_s02_whole_operations_trace_step_dt << [jms_dwd__dwd_s01_whole_operations_trace_dt]
